Data Labelling in a Nutshell

"The biggest bottleneck for AI development is data. There's not enough of it, and it's expensive to get and label." Charles Wong, CEO of Bifrost

Data labelling encompasses assigning distinct labels to collected data and categorising it into specific groups. For instance, images containing people's faces are separated; each individual is identified and tagged with a name. This labelling process is conducted on extensive data, encompassing diverse identifiers and millions of labels. ML models then utilise these labels to make predictions based on the data pointers. Any errors in the labelling process can lead to inaccuracies in the models. However, market players involved in collecting and labelling data can be of much help in addressing such challenges towards enhancing the efficiency of current ML models. The ever-increasing popularity of the Internet and smartphones is constantly reshaping the business industry, bringing an increase in the utilisation of both social media marketing and data-driven decision making. Data from various sources, including social media, drive business intelligence strategies today.

Consequently, there has been a substantial increase in data generation and utilisation, fueling the data collection and labelling market growth which is expected to grow to $9.07bln by 2028. This market is primarily driven by the adoption of machine learning, artificial intelligence, and big data, which require data annotators, software, and processes to create a foundation for ML models. However, collecting, sorting, and labelling different forms of data can take time and effort. To address these challenges, industry players are providing innovative solutions, such as cloud-based automated image organisation, customised platforms, and AI-driven data labelling. Retail, e-commerce, IT, and telecom sectors are among the prominent adopters of these solutions, capitalising on social media for marketing, consumer insights, and business growth. The major factors that are expected to drive the future growth of data collection and labelling markets include the increase in digital browsing and sales in e-commerce, and the increasing need for better and smarter data in the IT&C industry.

The current state of the art in data labelling involves a combination of cutting-edge technologies and crowdsourcing methods. Automated data labelling is now possible due to technological advancements, such as computer vision algorithms and natural language processing techniques. These automated approaches leverage ML models to recognise patterns and assign labels to data with little-to-none need for humans in the loop. However, manual data labelling remains crucial for complex and nuanced tasks that require human intuition and expertise. To handle large-scale labelling requirements, crowdsourcing platforms have emerged as a valuable resource. Crowdsourcing enables the distribution of labelling tasks to many individuals, often through online platforms, allowing faster and more cost-effective data annotation. Crowdsourcing not only leverages human intelligence but also facilitates diversity in labelling perspectives, enhancing the quality of the labelled data. This combination of advanced technologies and crowdsourcing has become the state of the art in data labelling, enabling efficient and accurate annotation at scale.